Labeled datasets of images are used to train computer vision recognition systems to identify categories of future images. To build a large labeled dataset of images, crowdsourcing may be used to combine the labeling efforts of many individuals. Since each image must be individually labeled, the cost of building a large labeled dataset is high.
An alternative way to build a large labeled dataset of images is to use an image search engine to search for images related to each desired label. However, the resulting image nets will contain outlier images and images related to different uses of a word. For example, a search for “apple” will return images of fruit and Apple™ products. Relative to manual labeling of images, the automation of the process lowers the cost but results in lower accuracy. Additionally, the image search engine itself must be trained before the process can begin.